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Causal associations between scapular morphology and shoulder condition estimated with Bayesian statistics
Pezhman Eghbali1, Osman Berk Satir2, Fabio Becce3
1Laboratory of Biomechanical Orthopedics, Ecole Polytechnique Fédérale de Lausanne, Institute of Bioengineering, Switzerland.
Computer Methods and Programs in Biomedicine
|February 26, 2025
Summary
Scapular morphology significantly impacts shoulder conditions like osteoarthritis and cuff tear arthropathy, with effects varying by sex. Understanding these anatomical variables can aid in early pathology detection and surgical planning.
Area of Science:
- Orthopedics and Sports Medicine
- Radiology
- Biostatistics
Background:
- Correlation between shoulder condition and scapular morphology is known but debated.
- Precise impact of anatomical variables requires further investigation.
- Scientific modeling is crucial before statistical analysis.
Purpose of the Study:
- Evaluate the causal association between scapular anatomy and shoulder conditions.
- Investigate the influence of sex, age, height, and weight.
- Focus on primary osteoarthritis (OA) and cuff tear arthropathy (CTA).
Main Methods:
- Utilized a deep learning model to calculate scapular morphology variables (AA, APA, ATA, GIA, GVA) from CT scans of 396 subjects.
- Applied do-calculus for causal association identifiability.
- Employed a Bayesian multinomial logistic regression model for estimation.
Main Results:
- Specific scapular angles (AA, APA, ATA, GIA, GVA) showed varying impacts on OA and CTA probabilities.
- Morphological effects were generally more pronounced in females than males.
- Acromion angle and glenoid inclination angle demonstrated the most significant effects.
Conclusions:
- A Bayesian causal model was developed to assess scapular anatomy's effect on shoulder conditions.
- Results align with clinical knowledge, suggesting potential for early pathology detection.
- Findings can inform and optimize surgical planning in clinical settings.
